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Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

This chapter aims to highlight the existing applications and future prospects of Artificial Intelligence (AI) in the tourist business. In addition, this chapter investigates the obstacles in using AI in the Indian tourist industry.

To achieve the study's aims, both primary and secondary data are used. Using secondary sources, desk research was conducted to investigate the existing uses and future prospects of AI application in the global tourism industry. In addition, qualitative interviews with 25 executives in the Indian tourist business were undertaken to study the obstacles to using AI in the Indian tourism industry.

The research found that the applications of AI in the worldwide tourist business are extensive. Nonetheless, corporations are actively using AI-based technology to improve the customer experience via chatbots, intelligent forecasting and smart, tailored travel experiences. The qualitative interviews found that the implementation of AI technology in the Indian tourist industry is hindered by budgetary restrictions, knowledge constraints and barriers relating to human resources.

The use of AI in the tourism business may significantly improve the client experience. As a consequence, the use of AI-based chatbots and intelligent travel aides is growing exponentially. The research examined the many uses of AI in the worldwide tourist industry as well as the obstacles associated with the deployment of AI in the Indian tourism industry.

The impact of artificial intelligence (AI) and extended reality (XR, including virtual reality [VR], augmented reality [AR], and mixed reality [MR]) on marketing in Industry 5.0 and Society 5.0 is explored with systematic literature review in this chapter. AIXR is becoming a necessary aspect of marketing, driven by efficiency, productivity, and innovation. Despite AI's capabilities, the human touch in marketing is preferred due to superior adaptive, creative, and innovative abilities. The use of fully automated marketing systems is limited to specific tasks. This research will benefit both practitioners and academics focusing on AIXR in marketing and is limited by the number of included literature.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.

Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.

Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.

This chapter suggests that enhancing sustainable development in the age of technologies requires reflection about the relationship between business practice and sustainable development, as well as clarification of the relationship between sustainability and sustainable development. At the core of business activity is the definition of sustainable development defined by Brundtland (1987) as ‘meet[ing] the needs of the present without compromising the ability of future generations to meet their own needs’. Although that captures only one aspect of the sustainability story and its relationship to sustainable development, it nonetheless shapes business approach in research and in sustainability practices. To illustrate the contradictions and tensions in practice so far, this chapter uses three lenses: measurement in environmental, social and governance (ESG) investment, the problem of scalability and the challenge of bias in artificial intelligence (AI). It is not clear that we need a paradigm shift, but a shift in mindsets around sustainability business practice will be needed if sustainable development is to be enhanced in the age of technologies.

The contemporary business environment has been revolutionized by artificial intelligence (AI), which has undoubtedly impacted the operational and competitive strategies of small and medium-sized enterprises (SMEs). In this write-up, we delve into AI’s influence on SMEs by delving into the advantages, obstacles, and prospects associated with adopting AI technology. Based on an exhaustive analysis of current literature, this essay contends that SMEs can optimize their functions, enhance decision-making processes, and deliver superior customer experiences with the adoption of AI. However, SMEs face several challenges in embracing AI technology, including the high cost of implementation, lack of technical expertise, and concerns about data confidentiality and security. This chapter also illuminates prospective opportunities that SMEs can leverage through AI, such as establishing a foothold in new markets and competing effectively with larger enterprises. Overall, this chapter concludes that SMEs must carefully evaluate the costs and benefits of adopting AI, and develop a strategic plan for implementation that aligns with their business goals and resources.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.